Wrestling the One-Time Buyer Syndrome
Marketers have different names for them. Some marketers call them “One-and-done” customers. Others call them by more innocuous “1-Time Buyers” phrases. The latter is the literal description of what they are. But I call them “problems” — potential or immediate.
Considering how much marketers spend to acquire any new customer, those one-timers pose real challenges. In the metrics-governed marketing world where ROI means everything, they put marketers in the corner from the beginning.
“Great! Someone just walked in, bought and walked out with merchandise! But will we ever recover the acquisition cost from them? We gave them a fat 20% discount just for showing up!”
In the old days — not too long ago, though — marketers used to plan to break even on new customers on their second or third purchase. Now, no one seems to have that kind of patience in the fast lane, where “everything, all the time” is the norm and the consumers are distracted constantly by competing offers and messages. Hence, many retailers put out an ambitious goal of breaking even at “hello.”
The Customer Acquisition and Retention Challenge
That translates into good news for low-cost acquisition channels, like email or Facebook, and bad news for relatively expensive channels, like direct marketing or traditional media. Regardless of channel usage, however, marketers must be smart about both retention and acquisition. In other words, they must stop the bleeding and pump in new blood at the same time.
I often see that one-time buyers make up over 80% of the customer base of a retailer. Even when we go back four to five years and count every transaction, the lowest figure that I’ve seen hovers around 60% or so.
That means, even in an unusually decent case, more than half of new customers do not come back. Pretty scary stuff.
What Marketers Can Do to Retain Customers
If that figure goes over 80%, I recommend starting with a more refined acquisition strategy. Simply because without new blood coming in, there won’t be much to talk about in the near future.
The first thing that I would ask is how aggressive the marketers want to be in terms of channel usage. I’ve seen bold ones who go the multichannel route with varying degrees of cost-friendliness, and conservative ones who would stick only with cheap and measurable channels.
To Retain, Acquire Customers Intelligently
Regardless of the degree of aggressiveness, the first concern is if they have been targeting the “right” prospects.
Years of experience in data and analytics business taught me that not all customers are created equal. You may have multiple pockets (or segments) of vastly different types of customers in your base, starting with the most valuable customers to downright barnacles who are professional bargain-seekers with no chance of being a loyal customer.
Going after the right kind of customers during the acquisition stage will curb the one-time buyer problem.
Whether you want to toss a bunch target samples to Facebook, go to third-party data vendors or join a co-op for modeled prospects, I strongly suggest marketers define the ideal target for them first.
- When you say “valuable,” what does that really mean?
- In terms of frequency, is that measured by the number of transactions or days between transactions?
- In terms of dollars, is that in total customer value or average spending level per transactions?
There are many ways to do it, and what I am suggesting is to try them as many as you can — when it comes to target definitions — and keep testing them. Targeting requires adjustment of the gunsight, with many rounds of practice shots.
One of the tricks I’ve learned while being a vendor all of my life is that you never try one method, one channel or one type of target. Because, if that “one” thing fails, you’ll be fired. Simple as that. But if you try three to four different combinations of target definitions and methodologies, then you have a better fighting chance to stay in the game, thanks to cumulative learning. After all, 1:1 marketing is all about learning from the past endeavors, isn’t it?
Here’s What I Recommend
So, I recommend trying different types of targeting (i.e., target definition of any “look-alike” modeling or simple selects) in different focus areas. For example:
- Behavioral Targeting: Target after you your audience’s best behavior, however you define them. I would use separate measures, such as transaction frequency and dollar amount, as responsiveness is often inversely related to sheer value (e.g., an infrequent visitor who spends a lot in one transaction).
- Demographic Targeting: What do those most valuable customers look like? What demographic clusters do they belong to, and what are their key demographic profiles? This type of targeting may not be as precise as behavioral targeting, but basic segmentation often provides a common language among disparate players in the acquisition play, including copywriters who would come up with relevant messages for each segment. Commonly defined clusters also open doors toward new target areas (e.g., targeting Millennials when an existing target base is mostly in older age segments).
- Regional Targeting: It is not unusual to see a high concentration of customers around physical store locations, even for online traffic. Test in and out of traditional footprints for an effective expansion strategy by channel.
- Product Targeting: Depending on the product lines, you may be dealing with vastly different customer profiles. Customer profile by high-level product category is important, as it is not a good idea to have a one-size-fits-all type of targeting when you carry distinct lines of products. The average of multiple types of customers is really nothing; there are no such things as “average” customers, when they are separated in dichotomous universes.
There are many ways to slice and dice this, but the important thing is to let the ideas fly within reason (i.e., don’t overdo it, either). And at some point, you will run out of options just using RFM segments, so plan to dive into look-alike models; many list vendors and social media publishers offer modeling, either in forms of traditional models or machine learning. But even the most cutting-edge targeting engines won’t work if the target is way off. Attracting barnacles is just one example.
Now Retain Those Customers
Then I would turn the attention to the retention side to curb this one-time buyer problem. But this time, I suggest marketers look at it not just from the segment/targeting point of view, but from the timeline point of view, as well.
Targeting based on behavioral profiles may look like this:
- High-Value Customers, based on dollar amount (even for one purchase). The top 10% to 15% of the customers in terms of spending level, for example.
- Study Frequent Shoppers and/or Visitors (who are not necessarily the same as high-value customers), and mimic their profile among one-time buyers.
- New-to-the-Brand Customers. Don’t treat all one-time buyers the same way, as they may not have had enough time to show their full potential yet.
- Multichannel Customers. Identify differences in profiles between online and offline customers, and also follow after profiles of multichannel users.
- Customers Who Bought Multiple Product Types or Multiple Brands (they are the best targets for cross-sell/up-sell models). Model after them and sort out high scores among one-time buyers.
- Profile Customers With a Specific Product Purchase (e.g., strategic “entry” product buyers, targeting by distinct product lines, etc.), and treat look-alikes of them with different offers.
- Separate Full-Price Purchasers bargain seekers (using % of transactions in various discount banding, including 0% discount), and create a probability model on the “cheapness” spectrum (i.e., “likely to be a bargain-seeker” moving toward “likely to be full-price purchaser”).
- Seasonal Buyers (peak vs. off-season). Do not ignore offseason shoppers — whenever that is for you — as they may open up new opportunities. In fact, omitting Christmas-only buyers from analytics may reveal interesting patterns.
- And of course, analyze one-time buyers in depth, to define profiles of “non-target.”
I’m sure many marketers have been playing with this type of target for years. Now let’s add the timeline view:
- Actives — Recent buyers or what some call “hot line names.”
- In-Market — (e.g., 60 days since the last purchase, depending on average number of days between transactions for the base)
- Faders — Need to be gently nudged.
- At-Risk — Bribe them if you have to, before they go dark.
- Inactive — Dormant customers. Treat them as if they are prospects, but with some “dated” transaction data (which is still better than nothing).
Marketers who still “batch and blast” indiscriminately should really look at life stages of the customers more carefully. Are you bombarding the recent buyers with two emails a day, and not doing enough to wake up the dormant customers? Even if one is not building some optimization model for every stage of the game, just by treating these basic segments (based on moving timeline) differently will improve the situation.
Yes, you may have to bribe old customers to come back. But the important takeaway is never to treat everyone the same way. And if you use this two-dimensional approach, you will be able to create enough varieties in targeting and messaging without investing heavily on deeper analytics.
Now, going back to that one-time buyer problem, marketers should not treat all of them the same way. Some may be just new, and haven’t even had a chance to come back to you yet. Some may be aging really rapidly, and you must act on them now. Some may have gone dormant, but in that one purchase, she may have spent a lot of money. And depending on what they have bought through what channel, you need to treat them differently. If they took a fat discount and never came back, well, you may have to curb the urge to send more discount offers and just let them go.
When there is a big fat problem in front of us, it always helps to break it down into multiple categories of problem areas. You will win some and lose some in the beginning. But if you really apply that time-tested “closed-loop marketing” principle, things will get better, one metric at a time.
And the one that you really have to watch out for? “Number of transactions per customer,” as it is proven that multi-buyers bring the most revenue to retailers. That one metric that is not up for debate.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.